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Beyond diagonal-IRS assisted UAVs in terahertz network utilizing twin-delayed deep deterministic policy gradient approach

Author

Listed:
  • Shereen S. Omar

    (Helwan University)

  • Ahmed M. Abd EL-Haleem

    (Helwan University)

  • Ibrahim I. Ibrahim

    (Helwan University)

  • Amany M. Saleh

    (Helwan University)

Abstract

This paper presents an innovative approach in Terahertz (THz) band communications in 6G network, utilizing the integration of a Beyond Diagonal Intelligent Reflective Surface (BD-IRS) and Unmanned Aerial Vehicle (UAV) communications. This work examines the significance of THz downlink communication system in achieving an optimal trade-off between system performance and the circuit topology complexity of BD-IRS. An optimization framework named a (BD-IRS UAV) that optimizes the beyond diagonal BD-IRS phase shift in the reflective mode and finds the optimal UAVs locations, aiming to maximize the system data rate has been proposed. The multi BD-IRS UAV formulated problem is modeled as a Markov Decision Process. Nevertheless, this optimization problem is a non-convex and it is difficult to solve optimally. To tackle this difficulty, a Deep Reinforcement Learning based framework utilizing Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm is proposed to achieve an efficient solution. Finally, simulation results demonstrate the emphasis for the proposed robust algorithm-assisted THz system which significantly improves the system data rate and impacts on performance of the system when compared with the benchmark approaches.

Suggested Citation

  • Shereen S. Omar & Ahmed M. Abd EL-Haleem & Ibrahim I. Ibrahim & Amany M. Saleh, 2025. "Beyond diagonal-IRS assisted UAVs in terahertz network utilizing twin-delayed deep deterministic policy gradient approach," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(2), pages 1-20, June.
  • Handle: RePEc:spr:telsys:v:88:y:2025:i:2:d:10.1007_s11235-025-01283-1
    DOI: 10.1007/s11235-025-01283-1
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